In this fellowship, I aim to study how variable networks of proteins influence the development and incidence of complex metabolic disorders as a consequence of genetic, environmental, and gene-by-environment (GE) factors. To do this, I will examine metabolic tissues from 100 cohorts of the genetically-diverse BXD mouse population. During my doctoral studies, I examined the genetic bases driving variable development of diabetes, obesity, and other metabolic traits in a diverse mouse population on two different diets over the first 7 months of their lives. However, to understand the causes and mechanisms underlying the observed heritability in these variant phenotypes and diseases, it is necessary to obtain intermediary molecular data such as transcriptomics and proteomics. These analyses will be performed on four metabolic tissues: liver, quadriceps, heart, and brown adipose. Transcriptomics (e.g. microarray, RNA-seq) has been well-proven over the past decade, while proteomics is still emerging. Recently, SWATHMS proteomics has been developed and proven in the Aebersold lab in cell lines and yeast. This mass spectrometric technology facilitates systems proteomics on a scale an order of magnitude larger and with better technical reproducibility across broad populations than earlier approaches (e.g. discovery proteomics, called ?shotgun?). Due to the cutting-edge nature of this proteomics technology, and the observation that protein and transcript networks do not tightly correlate, SWATH-MS proteomics is expected to yield many new insights into even well-studied metabolic networks. In Aim 1, I plan to perform SWATH in four tissues in all 100 cohorts, develop protein relationships of correlation and causality (e.g. QTLs), and examine how data-driven networks can explain phenotypic variation, and how they compare against literature. Transcriptomics are being performed and provided by Prof. Williams and Prof. Auwerx (see letters of reference). In Aim 2, I will examine how SWATH-MS compares to discovery and SRM proteomics in the same samples in vivo, and how the distinct peptides that represent a protein may be used to identify posttranslational modifications and protein isoforms. In Aim 3, I will work on integrating proteomic data with transcriptomics and using combined network models to understand how a multilayered approach to systems biology can be used to understand metabolic variation beyond what can be seen by transcriptomics or proteomics alone.